IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2020i1p268-d473408.html
   My bibliography  Save this article

Artificial Intelligence Model of Drive-Through Vaccination Simulation

Author

Listed:
  • Ali Asgary

    (Disaster & Emergency Management, School of Administrative Studies, York University, Toronto, ON M3J 1P3, Canada
    Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada)

  • Svetozar Zarko Valtchev

    (Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada)

  • Michael Chen

    (Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada)

  • Mahdi M. Najafabadi

    (Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada)

  • Jianhong Wu

    (Advanced Disaster, Emergency and Rapid Response Simulation (ADERSIM), York University, Toronto, ON M3J 1P3, Canada
    Department of Mathematics and Statistics and Laboratory for Industrial and Applied Mathematics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the near future. Rapid mass vaccination while a pandemic is ongoing requires the use of traditional and new temporary vaccination clinics. Use of drive-through has been suggested as one of the possible effective temporary mass vaccinations among other methods. In this study, we present a machine learning model that has been developed based on a big dataset derived from 125K runs of a drive-through mass vaccination simulation tool. The results show that the model is able to reasonably well predict the key outputs of the simulation tool. Therefore, the model has been turned to an online application that can help mass vaccination planners to assess the outcomes of different types of drive-through mass vaccination facilities much faster.

Suggested Citation

  • Ali Asgary & Svetozar Zarko Valtchev & Michael Chen & Mahdi M. Najafabadi & Jianhong Wu, 2020. "Artificial Intelligence Model of Drive-Through Vaccination Simulation," IJERPH, MDPI, vol. 18(1), pages 1-10, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:268-:d:473408
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/1/268/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/1/268/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eva K. Lee & Ferdinand Pietz & Bernard Benecke & Jacquelyn Mason & Greg Burel, 2013. "Advancing Public Health and Medical Preparedness with Operations Research," Interfaces, INFORMS, vol. 43(1), pages 79-98, February.
    2. Nathaniel Hupert & Alvin I. Mushlin & Mark A. Callahan, 2002. "Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers," Medical Decision Making, , vol. 22(1_suppl), pages 17-25, September.
    3. Michael F Beeler & Dionne M Aleman & Michael W Carter, 2014. "A simulation case study to improve staffing decisions at mass immunization clinics for pandemic influenza," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(4), pages 497-511, April.
    4. Adrian Ramirez-Nafarrate & Joshua D. Lyon & John W. Fowler & Ozgur M. Araz, 2015. "Point-of-Dispensing Location and Capacity Optimization via a Decision Support System," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1311-1328, August.
    5. Fares Qeadan & Trenton Honda & Lisa H. Gren & Jennifer Dailey-Provost & L. Scott Benson & James A. VanDerslice & Christina A. Porucznik & A. Blake Waters & Steven Lacey & Kimberley Shoaf, 2020. "Naive Forecast for COVID-19 in Utah Based on the South Korea and Italy Models-the Fluctuation between Two Extremes," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Davide Barbieri & Enrico Giuliani & Anna Del Prete & Amanda Losi & Matteo Villani & Alberto Barbieri, 2021. "How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(14), pages 1-10, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muckstadt, John A. & Klein, Michael G. & Jackson, Peter L. & Gougelet, Robert M. & Hupert, Nathaniel, 2023. "Efficient and effective large-scale vaccine distribution," International Journal of Production Economics, Elsevier, vol. 262(C).
    2. Fadaki, Masih & Abareshi, Ahmad & Far, Shaghayegh Maleki & Lee, Paul Tae-Woo, 2022. "Multi-period vaccine allocation model in a pandemic: A case study of COVID-19 in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    4. P. Daniel Wright & Matthew J. Liberatore & Robert L. Nydick, 2006. "A Survey of Operations Research Models and Applications in Homeland Security," Interfaces, INFORMS, vol. 36(6), pages 514-529, December.
    5. Eva K. Lee & Siddhartha Maheshwary & Jacquelyn Mason & William Glisson, 2006. "Large-Scale Dispensing for Emergency Response to Bioterrorism and Infectious-Disease Outbreak," Interfaces, INFORMS, vol. 36(6), pages 591-607, December.
    6. Ubaid Illahi & Mohammad Shafi Mir, 2021. "Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11157-11178, August.
    7. Amir Ardestani-Jaafari & Beste Kucukyazici, 2022. "Improving Patient Transfer Protocols for Regional Stroke Networks," Management Science, INFORMS, vol. 68(9), pages 6610-6633, September.
    8. Acar, Müge & Kaya, Onur, 2019. "A healthcare network design model with mobile hospitals for disaster preparedness: A case study for Istanbul earthquake," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 273-292.
    9. Joseph R. Egan & Richard Amlôt, 2012. "Modelling Mass Casualty Decontamination Systems Informed by Field Exercise Data," IJERPH, MDPI, vol. 9(10), pages 1-26, October.
    10. Jones, Dylan & Firouzy, Sina & Labib, Ashraf & Argyriou, Athanasios V., 2022. "Multiple criteria model for allocating new medical robotic devices to treatment centres," European Journal of Operational Research, Elsevier, vol. 297(2), pages 652-664.
    11. Vatsa, Amit Kumar & Jayaswal, Sachin, 2016. "A new formulation and Benders decomposition for the multi-period maximal covering facility location problem with server uncertainty," European Journal of Operational Research, Elsevier, vol. 251(2), pages 404-418.
    12. Feng, Keli & Bizimana, Emmanuel & Agu, Deedee D. & Issac, Tana T., 2012. "Optimization and Simulation Modeling of Disaster Relief Supply Chain: A Literature Review," MPRA Paper 58204, University Library of Munich, Germany.
    13. Areej Alhothali & Budoor Alwated & Kamil Faisal & Sultanah Alshammari & Reem Alotaibi & Nusaybah Alghanmi & Omaimah Bamasag & Manal Bin Yamin, 2022. "Location-Allocation Model to Improve the Distribution of COVID-19 Vaccine Centers in Jeddah City, Saudi Arabia," IJERPH, MDPI, vol. 19(14), pages 1-21, July.
    14. Eva K. Lee & Fan Yuan & Ferdinand H. Pietz & Bernard A. Benecke & Greg Burel, 2015. "Vaccine Prioritization for Effective Pandemic Response," Interfaces, INFORMS, vol. 45(5), pages 425-443, October.
    15. Pan, Yuqing & Cheng, T.C.E. & He, Yuxuan & Ng, Chi To & Sethi, Suresh P., 2022. "Foresighted medical resources allocation during an epidemic outbreak," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    16. R. K. Jha & B. S. Sahay & P. Charan, 2016. "Healthcare operations management: a structured literature review," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(3), pages 259-279, September.
    17. Douglas K. Owens, 2002. "Analytic Tools for Public Health Decision Making," Medical Decision Making, , vol. 22(1_suppl), pages 3-10, September.
    18. Sergio Contreras-Espinoza & Francisco Novoa-Muñoz & Szabolcs Blazsek & Pedro Vidal & Christian Caamaño-Carrillo, 2022. "COVID-19 Active Case Forecasts in Latin American Countries Using Score-Driven Models," Mathematics, MDPI, vol. 11(1), pages 1-17, December.
    19. Francesco Pilati & Riccardo Tronconi & Giandomenico Nollo & Sunderesh S. Heragu & Florian Zerzer, 2021. "Digital Twin of COVID-19 Mass Vaccination Centers," Sustainability, MDPI, vol. 13(13), pages 1-26, July.
    20. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "The benefits of combining early aspecific vaccination with later specific vaccination," European Journal of Operational Research, Elsevier, vol. 271(2), pages 606-619.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2020:i:1:p:268-:d:473408. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.